1. Field of the Invention
This invention relates to an image processing method and a computer readable medium for image processing for generating an image of a lesion part, an organ, etc., from volume data obtained by various image diagnosis apparatuses such as an X-ray CT (Computed Tomography) apparatus and an MRI (Magnetic Resonance Imaging) apparatus for medical use.
2. Description of the Related Art
Various image diagnosis apparatuses such as the X-ray CT apparatus and the MRI apparatus generate tomographic images of an inspection part from the volume data of a subject, and display an image on a monitor screen for diagnosis. For example, in a case of an organ of a circulatory system such as a heart or a vessel or any other moving organ, motions of tissues forming the organ are observed according to the tomographic images, and function of the organ, etc., is diagnosed.
As it is made possible to provide a high-resolution image in a short time by such an image diagnosis apparatus, a region of interest of an organ, a tumor, etc., is extracted from the obtained volume data. Furthermore, the region of interest is visualized in so as to be easily viewed or is quantified for measuring an area or a volume, whereby it is serviceable to diagnosis of the lesion part.
Hitherto, region extraction from volume data has been executed three-dimensionally with respect to the volume data. When a group of volume data including a plurality of phases (each phase corresponds to each volume data) exists, region extraction has been executed for each of the phases. The region extraction is to obtain a region of interest in each volume data. A plurality of volume rendering images according to the plurality of phases of volume data is displayed in order, whereby a moving image can be obtained.
FIG. 7 is a flowchart of an image processing method in a related art. In the image processing method in the related art, first a group of volume data V1 to Vn including a plurality of phases are acquired using the image diagnosis apparatus (step S21). Next, precise region extraction of the volume data V1 is executed so as to obtain reference data, and a region M1 is obtained (step S22). Then, registration (motion compensation, position adjustment, etc.) on the volume data V1 to the volume data Vn is performed (step S23).
Next, region extractions of the volume data V2 to the volume data Vn are executed using the extraction region M1 of the volume data V1 as the reference data, and regions M2 to Mn are obtained (step S24). Next, differential between regions among the regions M1 to Mn are respectively obtained, and the differentials are used so as to obtain an abnormal phase (phase wherein region extraction seems to results in failure) (step S25). When the abnormal phase is detected, the region extraction with new parameters is repeated until the abnormal phase no longer exists.
On the other hand, as for a two-dimensional region extraction method of executing the region extraction of a tissue in an ultrasonographic image, a slice image of an MRI image, etc., a method of performing extraction processing for each phase and then detecting erroneous extraction is known. That is, contours are extracted in each frame, areas (or volumes) of regions inside the contours are obtained, time changes in the areas (or volumes) are observed, and contour extraction is performed again for the part where abrupt change is observed (For example, refer to JP-A-9-299366).
However, in the image processing method in the related art shown in FIG. 7, a user command and an enormous amount of calculation are required for obtaining the region M1 by executing the precise region extraction of the volume data V1 at step S22. In order to perform the registration (motion compensation, position adjustment, etc.) on the volume data V1 to the volume data Vn at step S23, the user command and the enormous amount of calculation are also required. Further, the differential of regions between regions among the regions M1 to Mn are respectively obtained, and are used so as to obtain the abnormal phase at step S25. However, when deformation of the organ occurs while photographing, the abnormal phase cannot be obtained. This is because the differential region is obtained by making a comparison of raster data, and thus particularly when time resolution of a moving image is insufficient, correspondence between the regions is lost. For example, when a vessel moves in an image, and any part of the vessel does not exist in the same coordinates for each phase, continuity of raster data in the time direction does not exist and therefore it is difficult to detect the abnormal phase by the method in the related art.
In the image processing method in the related art, the precise extraction region information is created only for one phase as the reference, and the precise extraction result is used as the reference data in other phases. Therefore, it is difficult to make a comparison between the extraction results in other phases so as to detect an error. That is, the extraction result in each phase depends on the extraction region information as the reference.
In the image processing method in the related art, it is required that the calculation result at a first stage, particularly in the reference phase, be precise. Therefore, a great deal of calculation time and user labor for checking are taken, and the image of the region of interest cannot be displayed with good responsivity.
FIGS. 8A and 8B show images of executing region extraction of a heart and a vessel on a plurality of volume data in continuous time (phases 1 to 3). FIG. 8A shows ideal extraction results in phases 1 to 3. FIG. 8B shows actual extraction results in phases 1 to 3.
In the image processing method in the related art, the regions are compared directly as raster data, and thus particularly with respect to volume data, calculation amount increases and it becomes difficult to perform the registration. Therefore, it is difficult to perform a correction of region extraction result when the organ is being deformed in time series just by using the preceding and subsequent phases. Therefore, extraction failure parts are often generated as shown in phase 2 of the actual extraction results in FIG. 8B. Furthermore, most of registration algorithms require some region extraction result itself, in the processing process. It is difficult to provide sufficient registration in a state in which the region extraction result is not obtained.